Incepta Labs

This paper is also available at: doi.org/10.5281/zenodo…

Authorized disclosure under Averitas Holdings LLC → Incepta Labs sublicense pending. 
Original inventor and owner: Dr. Melinda B. Chu This paper is also available at: doi.org/10.5281/zenodo…

Agree; avoiding sycophancy and actively encouraging divergent analysis is essential for more trustworthy human–AI reasoning. Just published: “An Antidote to Sycophancy: Toward Epistemic Divergence in Human–AI Reasoning” It introduces MODAC identical prompts run across independent LLMs (different vendors, tabula-rasa conditions). Divergence is deliberately preserved as diagnostic signal, not noise. Human remains the final adjudicator. It is an LLM corollary to Medical Grand Rounds. doi.org/10.5281/zenodo… x.com/inceptalabs/st…

- Drafted a blog post - Used an LLM to meticulously improve the argument over 4 hours. - Wow, feeling great, it’s so convincing! - Fun idea let’s ask it to argue the opposite. - LLM demolishes the entire argument and convinces me that the opposite is in fact true. - lol The LLMs may elicit an opinion when asked but are extremely competent in arguing almost any direction. This is actually super useful as a tool for forming your own opinions, just make sure to ask different directions and be careful with the sycophancy.

This blew my mind. 🤯 I always thought based on media / hype that with new AI /Tech Bio companies were that you have idea, start the project, get list of compounds, synthesize them, ➡️ automated lab ➡️ Phase 1 human study, and then “cure ALL diseases in 5-10 years.” That’s what people say. 🚨 So I figured most TechBio would have to pivot to drug development b/c that’s wheee th real value in Biotech is and there’s not enough customers (other than Big Pharma) for all these SaaS. 🚨BUT looking into this, I learned that most companies only do 1 small step in drug design, so in theory you would need 6-10 of them to actually get to compound synthesis. 🚨This means 2 things: 1.). Means they can’t really pivot to developing drug assets b/c it’s only 1 small step of process. 2.) Reinforces my view that there aren’t enough customers for each SaaS 🌟 Check out this technical paper where I go into this in more detail and demonstrate my multi-model orchestration approach.

@jack Documentation and Validation of Human Provenance in in Human+AI era will be most important. x.com/inceptalabs/st… Human Conception Ledger (HCL) x.com/inceptalabs/st… Team Contribution Attribution Ledger (HCL) x.com/inceptalabs/st…

is the future value of "open source" code anymore? i believe it's shifting to data, provenance, protocols, evals, and weights. in that order.

This is also available at: doi.org/10.5281/zenodo… Authorized disclosure under Averitas Holdings LLC → Incepta Labs sublicense pending. 
Original inventor and owner: Dr. Melinda B. Chu

Authorized disclosure under Averitas Holdings LLC → Incepta Labs sublicense pending. 
Original inventor and owner: Dr. Melinda B. Chu

This is also available at: doi.org/10.5281/zenodo…

Authorized disclosure under Averitas Holdings LLC → Incepta Labs sublicense pending. 
Original inventor and owner: Dr. Melinda B. Chu.

This is right, but incomplete. The missing layer is human contribution provenance. 👩🏻‍💻💪 In a world of AI-generated outputs, knowing *who* actually contributed the core idea or result will matter as much as data or weights. We’re building frameworks like HCL (Human Conception Ledger) and TCAL (Team Contribution Attribution Ledger) to define human+AI contribution and define ownership in this new era. 👩🏻‍💻🤖

Authorized disclosure under Averitas Holdings LLC Disclosed via Incepta Labs LLC (pending license). Original inventor and owner: Dr. Melinda B. Chu. Priority claims trace to Nov 2023 → June 2024 → Aug 2024 → May 2025 patent families and supporting records. Documentation and provenance maintained via HedyNova and the Human Conception Ledger™

Interesting new paper: current AI agent groups often cannot reliably agree even on simple decisions.* This is one reason we often don’t optimize for forced consensus. It doesn't work and more importantly, disagreement can be more informative than fake agreement! In our work, we developed frameworks like MODAC** to surface disagreement, and sometimes Tabula Rasa (forced no-context resets) to get cleaner independent analysis. *If agent swarms can't reliably agree on simmple decisions and it gets worse as they get bigger, while his does model people in that way (big committees are less efficient than small groups), but that makes it actually less efficient to have large swarms. 🤔 **MULTI-ORIGIN DIVERGENCE ADVERSARIAL COUNCIL